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Tilburg University

Director Networks and Takeovers

Renneboog, L.D.R.; Zhao, Y.

Publication date:2013

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Citation for published version (APA):Renneboog, L. D. R., & Zhao, Y. (2013). Director Networks and Takeovers. (CentER Discussion Paper; Vol.2013-056). Finance.

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https://research.tilburguniversity.edu/en/publications/04ccf531-c446-4257-97e7-426b570aad56

No. 2013-056

DIRECTOR NETWORKS AND TAKEOVERS

By

Luc Renneboog, Yang Zhao

11 October 2013

ISSN 0924-7815 ISSN 2213-9532

0


Luc Renneboog 1

CentER, Tilburg University

and

Yang Zhao 2

Cardiff Business School, Cardiff University

Abstract We study the impact of corporate networks on the takeover process. We find that better connected companies are more active bidders. When a bidder and a target have one or more directors in common, the probability that the takeover transaction will be successfully completed augments, and the duration of the negotiations is shorter. Connected targets more frequently accept offers that involve equity. Directors of the target firm (who are not interlocked) have a better chance to be invited to the board of the combined firm in connected M&As. While connections have a clear impact on the takeover strategy and process, we do not find evidence that the market acknowledges connections between bidders and targets as the announcement returns are not statistically different from those bidders and targets which are ex ante not connected.

Keywords: Mergers and Acquisitions, Director Networks, Centrality, Connections.

JEL Classification: D85, G14, G34

Acknowledgments: We are grateful to John Armour, Lucian Bebchuk, Jan Belle, Bernie Black, Riccardo Calcagno, Salim Chahine,

Stijn Claessens, Mark Clatworthy, Martijn Cremers, Rafel Crespi, Peter Cziraki, Eric van Damme, Hans

Degryse, Ingolf Dittmann, Piet Duffhues, Andrew Ellul, Allen Ferrell, Fabio Feriozzi, Fabiozi Ferri, Philipp

Geiler, Marc Goergen, Willemien Kets, Christian Leuz, Neil O’Sullivan, Raghu Rau, Roberta Romano,

Christophe Spaenjers, Oliver Spalt, Konstantinos Stathopoulos, Jacques Vide, Paolo Volpin, and David Yermack

for helpful comments on this paper.

1 Department of Finance and CentER, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, the Netherlands, and European Corporate Governance Institute (ECGI), email: [email protected], phone: +31 13 4668210, fax: +31 13 466 2875. 2 Cardiff Business School, Aberconway Building, Colum Drive, Cardiff CF10 3EU, the UK, email: [email protected], Tel: + 44 (0)29 2087 6434, fax: + 44 (0)29 2087 4419,

1


1. Introduction

Traditionally, firms have invited top managers of other corporations or bankers to serve on

their boards of directors. Despite some restrictions in the UK Corporate Governance Code3,

interlocking directorships are still common in listed UK companies. The fact that executive

directors also occupy board positions in firms other than their own can create useful

connections not just at the personal (director) level but can also be valuable for firms.

Through such networks, directors develop and strengthen their personal (and social) ties,

which may lead to more influence in board room discussions. Furthermore, networks enable

directors to gather information about corporate strategies, sector trends, (macro-)economic

evolutions, but also about the evolution in executive remuneration, and managerial vacancies

in other companies.

Over the last decade, director networks have attracted growing academic attention in the field

of corporate finance and corporate governance. With the help of the network method based on

graph theory, studies have documented a positive link between networks and firm

performance (Geletkanycz and Boyd, 2011; Larcker, So and Wang, 2013). The main

argument is that networks provide better access to information from which the firm can

benefit in decision making (Omer, Shelley and Tice, 2012). More recently, researchers have

revealed previously hidden relationships between the connections of the corporate elite and

3 The Higgs report (2003) suggested that a full-time executive director of a listed company should not hold more

than one non-executive directorship and should not be Chairman of another listed company. Furthermore, no one

should be (non-executive) Chairman of two major (FTSE 100) companies. Within a company, a CEO should not

also hold the position of chairman. The report does not limit the number of non-executive directorship that one

can hold (in unlisted firms). At the end of 2003, the Higgs report was incorporated in the UK Corporate

Governance Code (formerly known as the Combined Code). It should be noted that firms adopt this code

voluntarily, which stands in contrast to the corporate governance developments in the US where the

Sarbanes-Oxley Act can lead to legal intervention in case of violations against the Act.

2

board room issues such as decision making on managerial compensation, hiring and firing of

top management, the recruiting of non-executive directors, and corporate restructuring. For

instance, Liu (2013) shows that a CEO’s connections (here labelled as ‘outside options’)

enhance his opportunities to leave his firm for another challenge. Cai and Sevilir (2010)

demonstrate in the context of mergers and acquisitions (M&As) that informational

asymmetries are lower when the bidder and the target have a common director. Renneboog

and Zhang (2011) and Horton, Millo and Serafeim (2012) demonstrate that a CEO’s direct and

indirect connections affect his power and his information-collection value, which is reflected

in a higher remuneration.

In this paper, we focus on how the connections of bidder and target firms impact on various

aspects of mergers and acquisitions (M&As) in the UK. In a network context, we study the

frequency of takeovers, the M&A process (in particular, the duration of the negotiation and

the success versus failure at the end of the negotiation process), the means of payment

(all-equity, all-cash or mixed offers), the retention or attraction of directors of the target firm

on the board of the merged firm, and whether there is a differences in terms of expected

returns at the announcement of connected and non-connected M&As.

To analyze the existence of director networks between bidder and target, we resort to

simulations of matching (potential) targets and bidders among all UK listed companies. To

find out the determinants of the various aspects of the takeover process mentioned above, we

use (multi-)nominal probit and tobit models, and as a robustness check sample selection

models. We find that when two firms are directly connected via their directors, the probability

that they merge or that one firm takes over the other is significantly higher than when the

firms are not connected. Takeover activity is not only affected by direct links with other firms

but also by the indirect connections of the board and of the CEO of the bidding firms: if those

(executive) directors hold many connections, acquisitions frequently occur. So, better

connected companies are more active bidders. We demonstrate that when a bidder and a target

have one or more directors in common, it is more likely that the takeover transaction will be

successfully completed. In this context, only direct connections play a role (and not the

3

indirect proxies for information collection). Furthermore, connections also significantly

reduce the time spent in the negotiation process (both for successful or failed negotiations). It

is possible that connections lead to an informational advantage which may translate into more

trust between the parties involved, as connected firms more frequently accept equity offers.

Directors of the target firm (who are not interlocked) have a better chance to be invited on the

board of the combined firm in the case of a connected M&A.

While connections seem to have a clear impact on the takeover strategy and process

(frequency, duration, successful completion, means of payment, board composition of the

combined firm), we do not find evidence that market acknowledges that some M&As are

connected (or that it matters in terms of expected value creation) because the cumulative

abnormal returns (CARs) around the announcement date are not statistically different from

zero.

This paper is organized as follows. In the next section, we review the existing literature and

formulate the hypotheses. In section 3, we present the descriptive statistics of the

(sub)samples. The results of the empirically analyses are discussed in section 4, and section 5

comprises our conclusion.

2. Hypotheses and Methodology.

The information value of director networks consists of directors’ ability to collect (non-public)

information about the potential target or bidder and about the potential synergies in an M&A.

Companies with better information access (through networks) are more likely to find valuable

targets and therefore initiate more takeovers. If the bidder or target are directly connected

through cross-directorships, these common directors may already have disclosed relevant

information about the potential benefits of an M&A prior to the negotiations such that the

negotiation process can be sped up. We formulate our hypotheses on the relation between the

information value of connections and the value, process, and performance of M&A in this

section.

2.1. M&A Frequency.

4

The first question we ask is whether the frequency of takeover bids initiated by a firm is

related to its network; in other words, do takeovers occur more often between firms with

common directors? It may be that firms who intend to take over another firm or want to be

taken over, offer a directorship to an (executive) director of a potential target or bidder,

respectively. This director may gain more information on the counterpart such that expensive

takeover mistakes can be avoided. The relation between takeover decisions and individual

director or corporate networks is not necessarily only based on direct links between bidder

and target as indirect director connections (links not directly with the respectively bidder or

target but through third boards) may also facilitate information transmission across companies.

Indeed, better connected companies are more likely to find suitable targets and engage more

frequently in M&As. Only few studies (Ishii and Xuan, 2010, and Wu, 2011) have related the

takeover probability to corporate (director) relations. Both studies focus on the US and show

that firm connectedness in an M&A sample is much higher than in random samples. We also

start our analysis with the method inspired by Ishii and Xuan (2010) and use simulation

techniques to create different samples from which hypothetical pairs of acquirers and targets

are selected. We expect that the level of connectedness is higher in the takeover sample group

than in the simulated samples, which leads to the following hypothesis: An M&A is more

likely to occur between two firms which are directly connected by means of common directors

(Hypothesis 1a) and for firms with a high indirect centrality scores which proxy for the

information collection ability of their directors in the universe of (listed) firms (Hypothesis

1b). This is translated into the following model:

(Cumulative) number of M&As = α+β1* (direct or indirect) centrality measure + β2* firm

characteristics + ε, whereby the centrality measure can be one of the following variables: the

bidder’s Degree, normalized Closeness measure, its Eigenvector centrality, and its

(normalized) Betweenness.4

2.2 Duration and Completion of M&A negotiations.

When the intention of the acquisition of a potential target is disclosed to the market by the

4 For the definitions of Degree and Closeness: see the methodology section. For the definition and calculation of other centrality measures (eigenvalue (a direct centrally measure) and betweenness (an indirect centrality measure)) which we use as a robustness check, see Renneboog and Zhao (2011).

5

bidder or the target, the target board needs to decide how to react and what advice (rejection or

acceptance) to give to its shareholders. Upon a negative response by the target board, the

bidder may make a sweetened offer or initiate a hostile takeover. As a reply, the target

company may consider accepting an upwardly revised offer or ask permission to the

shareholders on an extraordinary general meeting to activate various defensive mechanisms

to protect itself (Goergen et al., 2005; Martynova and Renneboog, 2008b, 2011b). The

duration of the M&A process (from announcement to deal completion) can be measured. The

bidder usually prefers to have a short negotiation duration, as a longer waiting time due to the

target’s resistance increases the transaction costs and uncertainty. Moreover, connections

between bidder and target may also have an impact on the negotiation duration in case of

unsuccessful M&As; connections also resolve the information asymmetry problem and

enable the parties involved to reach the end of negotiation (in this case: the bidder withdraws)

within a shorter period of time. In sum, we expect that director connections shorten the

negotiation time, thanks to the directors’ information about the counter party or the indirect

information value of their network. We also expect networks to have an impact on the

completion rate of the negotiation process. The completion rate stands for the frequency of

successfully rounding off the M&A process with a signature that the two firms will be merged.

M&As of firms involving direct connections or bidders with strong information gathering

potential (high indirect centrality) successfully reach the end the M&A process more

frequently (Hypothesis 2a). M&As of firms with direct connections and firms with strong

information gathering potential (high indirect centrality) experience a shorter takeover

duration process as well (Hypothesis 2b).

The duration of the M&A negotiation period is counted as the number of days starting from

the day on which an M&A intention is first publicly disclosed, until the transaction is

completed (the contract is signed) or the negotiation is abandoned. In some cases, we cannot

determine the negotiation time as the first public announcement that takeover negotiations

have taken place only occurs upon completion of the deal. We treat these observations

separately in our study.

6

2.3. Payment Method.

An important aspect of the negotiation relates to the payment method. An M&A could be

concluded in cash, in equity, or in a mixture of both. Information asymmetries between bidder

and target are an important determinant of the means of payment in corporate acquisitions

(Renneboog and Martynova, 2009). In particular, uncertainty about the true value of the target

firm induces the bidder to pay with its own equity rather than cash. Capital participation in the

combined firm makes the target shareholders share the risk of potential downward

revaluations after the bid’s completion. Faccio and Masulis (2005) document that a change in

the corporate control structure – for instance, by means of voting power dilution or the

emergence of an outside blockholder - may discourage bidders from paying for the

acquisition with equity. Thus, the likelihood of an equity payment is determined by the control

structures of the bidding and target firms. In particular, a cash payment is strictly preferred to

an equity payment when the target’s share ownership is concentrated and a bidder’s largest

blockholder only holds an intermediate or low level of voting power. This preference is

weakened if the target company is widely held or if the bidder’s dominant shareholder has a

supermajority of voting rights.

From a target shareholders’ perspective, the difficulty related to an all-equity offer lies within

the uncertainty about bidder’s stock value. An equity offer can be interpreted by the target as a

signal that the stock of the bidder is overvalued. This offer could therefore extend the

negotiation process as more detailed information on the bidder is to be gathered. If there are

common directors between bidder and target, we expect that the target is able to assess the

bidder’s stock value more accurately –overvalued or not - and will be more willing to accept

an equity offer (at the right offer rate). There are many studies on the payment method, but

none, save Wu (2011), mention the effect of director networks. Wu (2011) finds that

connections between bidder and target increase the likelihood of using a stock payment by

18.5%. Similarly, we hypothesize that: In M&As with direct connections between acquirer

and target, offers involving equity occur more frequently (Hypothesis 3).

2.4. M&A Performance.

A key issue in this paper is that direct and indirect connections at the firm level (and the

7

individual director level) create an informational advantage which implies that the acquirer is

able to select better acquisitions, which is in turn reflected in the creation of more value. The

question is therefore whether the market recognizes that the bidder makes a connected

acquisition. If the market is aware of this type of M&A and is convinced that the bidder is

hence unlikely to waste resources through an unsuccessful takeover, the bidder’s abnormal

stock return will be significantly positive upon the announcement of such an acquisition. In

contrast to the abnormal announcement returns of the target which typically are in the range

25%-35%, we know that the bidder’s announcement returns are in general very close to zero,

either slightly negative or slightly positive but on average not statistically different from zero.

A comprehensive overview of long and short term M&A returns for bidders and targets

around the world since the early 20th century can be found in Martynova and Renneboog

(2008a). If the bidder has a well-connected board and is hence better informed, the bidder’s

CEO may be less likely to succumb to building empires through M&As at the expense of

value creation. Evidence supporting this hypothesis has been documented for the US by Cai

and Sevilir (2010). Keeping in mind the benchmark of zero CARs for the bidder around the

announcement data, we hypothesize: In M&As with direct or indirect connections between

acquirer and target, the acquirer’s CARs are significantly positive (Hypothesis 4).

The alternative hypothesis is that connected M&As destroy value (in expectation) because a

connection may induce a false trust in the target. If connections are regarded as substitutes for

active information collection on the target such that the bidder’s estimation of the

compatibility between the bidder and the target and of the potential synergy value becomes

blurred, then connections induce poor takeover decisions. Furthermore, social connections

(e.g. decision makers in the bidder and target are friends) may contribute to the overvaluation

of the target. Therefore, acquiring a connected target may be considered as not efficient by the

investors such that a negative correlation between connections and announcement returns is

expected, which has been shown by Ishii and Xuan (2010) and Wu (2011) for US acquisitions.

Both studies show that connected M&As have lower bidder announcement returns than

unrelated M&As.

8

2.5. The Bidder’s CEO Compensation.

Renneboog and Zhao (2011) find a close relation between a CEO’s network and his

remuneration. They distinguish between different types of centrality variables and state that

direct measures represent managerial power or influence whereas the indirect centrality

measures capture the degree to which a CEO is able to gather valuable information. Both

types of networks are related to higher remuneration (higher bonus and higher equity-based

compensation), but they conclude that the direct network contributes most to excessive CEO

pay. In the context of this paper, an acquiring company may have contractually committed to

pay the CEO a bonus if he is able to complete successfully an acquisition. This creates strong

incentives for a CEO to acquire other firms. The question is here whether a CEO is using his

own connections and those of his firm to facilitate takeovers in order to get an acquisition

bonus subsequent to the acquisition. According to Grinstein and Hribar (2004), managerial

power is the primary driver of CEO bonuses following M&As. It should be noted that the vast

compensation literature doubts the independence of the CEO in the design of his

compensation contract, which would be especially the case for powerful CEOs with a long

tenure (Liu, 2013). Therefore, we hypothesize that CEOs obtain a higher bonus when they

undertake M&As facilitated by connections between acquirer and target (Hypothesis 5).

2.6. Target Director Retention

We examine whether the directors of the target company have a larger probability, subsequent

to the M&A, to be on the board of the combined company. If professional connections are

instrumental to bring M&As to a good end, connected directors of the target may have a

higher probability to be retained on the board of the merged firm. The professional (and social)

ties of the directors serving on both the bidder and target boards may lead to a higher number

of not-connected target directors to be invited to board of the combined firms. Some studies

document that the retention of the target CEO is positively affected by factors including the

abnormal stock return of the acquirer (Matsusaka, 1993) and social connections to target

company (Ishii and Xuan 2010). Hence, we expect that the target directors with no prior

connections to the bidder are more frequently invited to serve on the board of the combined

firm if there are connections between the acquirer and target (Hypothesis 6).

9

3. Sample Selection, Data Sources, and Descriptive Statistics.

Our M&A data are gathered from the Thomson One Banker SDC Premium database. We

collected information about 743 acquisition announcements that involved bidders and targets

both listed on the London Stock Exchange and took place over the period 1995 to 2012. We

collected stock price, accounting information (total assets, cash ratio, debt-to-assets ratio),

and other control variables (e.g. return on assets (ROA)) from Datastream as well as data on

the bidders’ and targets’ individual board members (e.g. the number of (non-)executive

directorships, cross-directorships between our M&A sample firms, ownership stakes by type

of shareholder) and on their board structures from the BoardEX database.

The first two columns in Table 1 show the size of our acquisitions’ sample and its distribution

over time. Most takeover announcements occurred in the periods 1998 to 2000, which

represents the climax of the fifth takeover wave (Martynova and Renneboog (2006, 2011a)),

and 2005 to 2007 which coincides with the recovery of equity market following its prolonged

slowdown triggered by the high tech collapse in 2000 (Goergen and Renneboog (2004)).

Columns (3) and (4) record the number and proportion of takeovers that are connected

through directors; a larger proportion of connected acquisitions occurred when the market for

corporate control was booming. On average, 9.4% of all acquisitions are connected (Column

(5)), a ratio is comparable to the US takeover samples in Wu (2011) and Ishii and Xuan (2011)

(6.38% and 10.60%, respectively). Table 2 depicts the number of acquisitions across

industries: takeovers are most frequent in the financial sector and the services industry (with

respectively, 28.94% and 21.27% of all bidders). Takeovers also occur often in manufacturing

and retailing sectors.

[Insert Tables 1 and 2]

The characteristics of the acquisitions such as connectedness, number of announcements per

bidder, takeover success rate, negotiation time, transaction size, means of payment in the offer,

and the market response to the takeover announcement are reported in Table 3. Many bidders,

namely 139 out of 513, have acquired/attempted to acquire more than one target throughout

10

our sample period (Panel A). Amongst them, some were serial bidders acquiring up to seven

target firms within our sample period. Panel B presents the target’s attitude towards to the

offer. In the UK market for corporate control, most M&As are friendly, only approximately 5%

of the deals are hostile takeovers (which are defined as deals with target board opposition –

whatever the reason). Most offers are all-cash offers (46.3%), almost a third are all-equity

deals (which include the largest transactions), and about 22% of the offers comprise a mix of

cash and equity (possibly also of loan notes) – see Panel C. It should be noted that our sample

includes all bids, both the successful transactions (609) and failed ones (134 deals ended with

the bidder withdrawing the offer). If we analyse the success rate conditional on the bidder and

target being connected through their directors (Panel D), we find that connected deals have a

substantially higher success rate than the unconnected ones (96% vs. 81%, the difference

being statistically significant at the 99% confidence interval). Panel E shows that in 9.4% of

the acquisitions, the bidder and target have at least one director in common, and on average

16.4% of all directors of the bidder and target boards serve on both boards (which implies that

some firms are connected through multiple directorships – more precisely, in the average

M&A transaction, the bidder and target have 1.74 directors in common). The statistics about

negotiation time are reported in Panel F. For the successful deals, the average time between

announcement and completion is almost two months (59.6 days)5, and in unsuccessful deals

the offer is withdrawn after a similar time period (of about 60 working days). Note that in

some rare cases, it can take up to ten months to finalize the transaction. The deal size amounts

to GBP 143 million, with the largest transaction amounting to GBP 1 billion (Panel G). The

median deal size (the value of the offer scaled by the market value of the bidder) amounts to

0.24, which indicates that the bidder is about four times larger than the target. Two thirds of

the transactions occur between two companies from the same industry, which we call focused

transactions. In panel H of Table 3, we summarize how the market receives the announcement

and show the CARs over event windows of different lengths ([-1,+1], [-5,+1] and [-10,+10],

whereby day 0 is the announcement day). We estimate the market model over the period 194

to 41 days before the announcement to get the systematic risk. In line with earlier research, the

5 This statistics is calculated after the 2% observations with longest negotiation time – which are prone to recording error - were removed from the sample.

11

announcement CARs [-1,1] for the bidder are indistinguishable from zero with a mean of

-0.47% and a median of exactly 0%. The 25% and 75% quartiles span a range of -2.36% to

1.34%. In the final Panel (I) we present statistics for the number of target directors joining

combined firm after the deal. On average, one director from the target company will remain

on the board of the combined company. Note this number does not include the ex-ante

common directors between bidder and target. Apparently, more target directors are invited to

serve on the board of the combined company if bidder and target had been connected prior to

the M&A.

[Insert Tables 3 and 4]

Table 4 exhibits the bidders’ characteristics. Degree and Closeness are used to measure the

bidder’s network centrality, but they capture different network properties. In order to

differentiate and compare the network advantage of the CEO and the entire company, we

calculate centrality measures on different levels. More specifically, Degree (C) measures the

number of director interlocks held by a company (hence the C-label); Degree (D) measures

the number of interlocks at the individual director level (hence the D-label) and we

concentrate on the network of the CEO in this study. Closeness (C) evaluates how close a

company is to all other companies in the network, while Closeness (D) takes the individual

director as a node. In order to define Closeness, we first create a matrix for all the companies

(directors) whereby each cell represents a connection between the companies (directors) or

lack thereof. A cell comprises a one in case of a connection and a zero in case of no connection.

We define the farness of a vertex as the sum of geodesic distances between this vertex and all

other vertices that can be reached. We transform the matrix into the geodesic distance matrix

by replacing all the zeros by the geodesic distance. A higher farness value indicates that the

vertex is further from other vertices. In order to define Closeness (and normalized Closeness),

we calculate the inverse of the sum of all geodesic paths from vertex v to any other vertex t:

∑=

),(

1)(

tvdvC

Gc

. In this formula, the Closeness centrality of vertex v (Cc(v)) is equal to one

divided by the sum of the lengths of geodesic paths (dG) from v to any other vertex t. A high

Closeness value reflects the shorter distance to all other vertices, which suggests that the

target vertex is more central in the network. The normalized Closeness is defined by the

12

following formula where n is the number of vertices in the graph: ∑

−=),(

)1(100)('

tvd

nvC

Gc

. A higher

normalized Closeness score implies a shorter distance to other vertices, in which case

companies (directors) may be able to acquire the information faster. The Closeness measure is

defined over all the connected vertices in the graph (which entails that all isolated vertices do

not have a closeness measure). Degree proxies for a firm’s (a director’s) direct ability to

collect information about the target and the bidder, whereas Closeness is an indirect measure

that shows how close a corporate (or director) node is to other nodes in the whole network of

corporations (directors). Therefore, closeness focuses on the general information collection

ability in the entire network, rather than access to information from interlocked companies.

The reason to differentiate the two is that according to social network theory, information

from close-by nodes are stronger but more likely to be redundant than that from distant nodes.

On average, the bidders in our sample have (executive and non-executive) directors who hold

directorships in six other companies, which is higher than the average Degree (4) of all listed

UK companies reported in Renneboog and Zhao (2011). Table 4 also reports the normalized

Closeness at the company level (C), and Degree and normalized Closeness at the director

level (D). In this paper, we focus on the connectedness of the CEO rather than other directors.

Therefore the centrality measures at the director level (D) are based on the CEO in that

financial year. The Degree measure at the director’s level is higher than at the corporate level

as is comprises the links with the directors of all the boards that directors is serving on. The

average bidder’s board size is 10.5 with a median of 10. The last four rows of Table 4 contain

bidders’ statistics on the ROA, cash ratio, debt ratio, and total assets (in million GBP).

4. Results.

4.1 The Frequency of Connected M&As.

If it is true that director networks increase the probability of M&As (Hypothesis 1), we expect

to find more connections between companies in the M&A sample than between randomly

matched companies. We therefore compare the level of connectedness of the M&A sample –

the pairs of bidders and targets in our original sample - to that of three other simulation groups

13

drawn from the universe of all listed UK firms. From the descriptive statistics, we know that

the probability of having at least one common director between a bidder and a target is 9.42%.

In the first simulation group, we match a bidding company in the sample to a potential target

company randomly selected from the industry of the real target in the year of the acquisition.

For instance, in 2007, company A acquired company B in the chemical industry. In the

simulation, we match company A to another randomly selected company C from the chemical

industry. By checking the board information of company A and the pseudo-target C in the

year 2007, we examine whether A and C have directors in common. This procedure is

repeated for all other bidders in the sample. When the pseudo-target happens to be the same

company (C = B) as the real target, we replace it with another company D until D ≠ B. The

second simulation group includes the targets from our sample matched with randomly

selected potential bidding companies from the industry of the real bidder in the year of

acquisition. The third simulation group includes random bidders and random targets selected

from the industry of the firms involved in the real M&As in the year of the M&A.

In simulation group 1 (Table 5), the percentage of directly connected companies is 4.38%;

this is significantly lower than the real percentage of connected firms in our M&A sample

(9.42%). When we match randomly selected ‘bidders’ (from the same industry as the bidder)

to the real M&A targets, we do not find any common directors between those bidders and

targets. Finally, less than 3% pseudo-bidders and pseudo-targets (both are randomly drawn

from the same industries as the bidder and the target) are connected. We conclude that the

average level of connectedness is much higher between the real M&A companies than

randomly paired companies, which supports our first hypothesis that connections matter in

M&As. Our simulation results are in line with the results from US data presented in Ishii and

Xuan (2010).

[Insert Table 5 about here]

We further examine the relationship between M&A activity and the level of connectedness.

First, we regress the total number of M&As that a bidder undertakes on the network centrality

of the bidder and other control variables (including board size, corporate performance, and the

financial structure). The independent variables are the average values for the whole sample

14

period. The result is reported in Panel A of Table 6: the Degree centrality measure at company

level (Average Degree (C)) is positively correlated with the number of M&As. This implies

that companies with many interlocking directors more frequently enter into M&A activity.

The (normalized) Closeness and the other centrality measures at the director (CEO) level,

have a positive but insignificant impact on the number of M&As. With exception of the

debt-to-equity ratio of the bidder, other factors including profitability, board size and structure,

and ownership structure (now shown) do not affect the cumulative number of M&As. In panel

B of Table 6, we take as dependent variable the cumulative number of deals over time, which

is the number of M&As that a bidder initiated since the start of our sample period up to a

specific point in time. The cumulative number-approach considers the M&A activities every

year while also taking into account the history of M&A activities and thus combines these two

measures: a dummy variable capturing an M&A transaction announcement in one particular

year and the total number of acquisitions that a bidder has undertaken over the whole time

period. We find that all centrality measures significantly increase the occurrence of an M&A.

This signifies that the when a bidder and its CEO have many connections (a high Degree), the

takeover activity of this bidder significantly augments. The same is valid when the firm is

strongly connected to the population of listed firms as expressed by its high Closeness.

Nevertheless, the above analysis cannot rule out one alternative argument that firms planning

expansions via acquisitions appoint well-connected directors to overcome information

asymmetries. In other words, instead of connectedness influencing M&A probability, it may

be other way around. If that is indeed the case, we expect to find that well-connected directors

(especially the ones connected with the target) are appointed shortly before the M&A occurs.

However, in the sample, the average tenures of the connected directors in the target firm is

more than 2.6 years. For most of sample (75%), the common director has been on the target

board for more than one year when the M&A is announced. On the bidder side, the common

director’s average tenure is 3.5 years. Therefore it is less likely that establishing connections

is solely driven by the purpose of an M&A.

To sum up, the analyses shown in Tables 5 and 6 yield strong evidence supporting the

hypotheses 1a and 1b. Namely, we find a positive relationship between takeover frequency

15

and connections through directorships between bidder and acquirer. Furthermore, not only

direct connections between bidder and target are important, but so are the indirect connections

of the board and the CEO of the bidding firms. In general, better connected companies are

more active in M&As.


4.2 M&A Completion Rate.

Better connected companies are more likely to engage in M&As. However, are these bidding

firms also more successful in completing the M&A negotiations (with a signature confirming

the creation of a combined firm)? In Table 7, we present the results of 6 logit models which

relate the takeover completion rate to difference measures of connectedness. We demonstrate

that when a bidder and a target have one (model (1)) or more (model (2)) directors in common,

the probability that the takeover transaction will be successfully completed significantly

augments. Models (3) and (5) show that bidders with a high Degree (bidders are connected to

many firms) are also more successful to bring the M&A negotiations to a successful end. It

should be noted that only the direct connections have an impact on the completion of the

M&A process which supports Hypothesis 2a. This is not the case for the indirect connections

which are captured by the normalized Closeness at the bidder and the bidder-CEO level

(models (4) and (6)). As predicted, hostile takeover negotiations have a higher chance to fail

and making a cash offer improves the odds to complete the transaction.


4.3 Duration of M&A negotiation.

While the previous section has shown that connectedness increases the probability to

successfully complete the deal, we now analyze whether the M&A negotiation time is

influenced by director connections. We expect that the time between the first public M&A

announcement and the completion of the negotiations (whether they are successful or not) are

shorter when the bidder and target share directors. Connections of this sort can improve the

information exchange such that less time is needed to complete the negotiations. As the

16

negotiation time is a left censored at zero for 18% of the sample, Tobit models are used in

Table 8. Panel A exhibits that both connection variables (the dummy capturing whether the

target and bidder are connected and the number of connections between bidder and target) are

significantly negatively related to the negotiation time. This implies that a connection

between bidder and target significantly reduces the time used to negotiate the deal. This can

result from the fact that bidder and target have already acquired much information prior to the

first public announcement of the bid and/or that the connections stimulate the trust in the

counterparty. This result is valid both for the subsample of successful M&A deals as well as

for the full sample including the deals with failed negotiations. Consequently, Table 8

provides strong support for Hypothesis 2b. In line with our expectations, Panel A also shows

that hostile takeovers trigger more resistance in the sample of ultimately successful deals.

When the offer includes equity, the valuation of the bidder’s equity may become an important

issue in the negotiation such that more negotiation time is required. We also show that larger

firms spend more time negotiating.

In Panel B of Table 8, we add bidder centrality at the company level to the models of Panel A.

A higher centrality measure, Degree (C), implies that many directors take directorships

outside the bidding company. In the context of the negotiation process with a target, we find

that a higher Degree prolongs the negotiation time. This suggests that a board with people

who hold many outside directorships may negatively affect the efficiency of decision making

due to lack of monitoring by this ‘busy board’, and may reduce the focus on (and increase the

duration of) the negotiations with the target firm. The Closeness (at the company level)

captures how close a firm is to important nodes in the network and is hence proxy for

information collection ability within the population of listed UK firms. A high Closeness

could imply that the bidding firm is better informed about the takeover opportunities in the

market which hence reduces the negotiation time. The statistical significance of Degree and

Closeness does not influence the significance of the Connected dummy variable and the

Number of connections.6

6 We also tested the effect of centrality measures on the individual level, but they do not have a significant

influence on negotiation time.

17

As a robustness test, we take the models of Panel A and substitute the variables capturing the

direct connection between bidder and target by – one at the time : (i) Degree at the company

level (C), (ii) Degree at the director (CEO) level (D), (iii) Closeness at the company level (C),

and (iv) Closeness at the director (CEO level (D). We find that the statistical significance

which we have found for these variables in Panel B does not change. Another robustness

check is survival analysis using hazard models on the sub-sample of non-zero negotiation

time observations. The result implies that connections shorten negotiation time (although

insignificantly so). Moreover, deals with more cash in payment and smaller bidder company

size on average take less time to complete.


4.4. The Means of Payment.

From an information value perspective, director networks that span the bidder and target

provide better information access, which may enable the target to evaluate the synergy value

as well the bidder’s equity value more accurately. We therefore expect that such connections

induce trust and that in connected M&A equity is more frequently used as payment. The

results of Models (1) in Panel A of Table 9 reveal that connections do indeed have a

significant and positive impact on the use of equity in an M&A offer, which supports

Hypothesis 3. Models (2), where the dependent variable is an indicator variable that equals

one if the offer consist of cash or is a mixed of cash and equity confirms that connections

reduce the use of cash in an M&A offer. Expectedly, an equity payment is more likely when

the relative transaction value is large and the bidder is smaller and less profitable as it is then

more difficult to raise the bid value in cash. In Panel B of Table 9, we use the percentage of

cash in the offer as the dependent variable. As before, we note that connections reduce the

need to offer cash. We also include the centrality measures Degree and Closeness, but they are

statistically insignificant. When we re-estimate the models of Table 9 using a multinomial

regression, we find results consistent with those reported above (not shown). Lastly, as the

final payment method may be influenced by the negotiation process, we apply a Heckman the

sample selection model to condition on negotiation failure, and the results remain valid. To

18

sum up, the empirical results on the offered payment method support the hypothesis that

equity is more likely to be used when bidder and target are connected.


4.5. M&A performance.

We study the bidder’s announcement CARs over a three-day event window [-1,1] (starting

one day before the first public announcement of the M&A (day zero) until one day after the

event) in order to examine whether connected M&As are expected to perform differently than

non-connected ones. We find that the bidders’ CARs of connected and non-connected M&As

are not statically different. In the regression models, both the variable Connection between

bidder and target (a dummy variable) and the total number of connections between them are

insignificantly related to the CAR, which implies that the market does not take connections

into account when they evaluate the M&A transaction (not shown). We also cannot find a

relation between connectedness measured by Degree and Closeness and the CARs. As

reported in the vast M&A literature on the means of payment (see Martynova and Renneboog,

2008, for an overview), we also find that an all cash payment is associated with more positive

market reactions. And a larger relative deal value and a larger bidder firm size also improve

the shareholders’ expected valuation of the deal (at the announcement). We conclude that we

reject Hypothesis 4; the market does not acknowledge the impact of connections on the M&A

process and valuation. An alternative explanation could be that even though connections may

be acknowledged by the market, their benefit does not outweigh their costs which are

reflected in insignificant expected returns.

4.6. The Bidder’s CEO Compensation.

We also investigate whether or not CEOs receive a higher remuneration after completing

connected M&As, while we control for corporate performance, CEO characteristics (tenure,

internal/externally hired, CEO-chairman duality), corporate governance variables (e.g.

ownership concentration, board structure), and financial information. We find that director

connections between bidder and target or the CEO’s Degree and Closeness are not a

significant determinant of his bonus, whereas to firm performance (ROA), CEO experience

19

(tenure), and firm size (total assets) do explain that type of remuneration. Hence, in this

sample of UK bidders, we do not find convincing evidence for a relationship between CEO

bonus and bidder-target connections and thus reject Hypothesis 5.

4.7. Retention of Targets’ Directors.

To examine the target’s director retention, we record the number of target directors who are

invited as directors on the board of the merged firm and regress this dependent variable on

variables capturing the connectedness of bidder and target. In order to avoid the identification

error that interlocked directors are already a director on the bidder’s board (and thus on the

combined firm’s board), we only count the number of retained directors that are not already

on the bidder’s board prior to the acquisition announcement. I.e., a director is only identified

as a retained director if he joined the company after the deal’s completion. When focus on

Total Retention (model (1) of Table 10), we notice that the coefficients of both our connection

variables are significant and positive. This implies that the target directors (without prior

connections to the bidding firm) have a better chance to remain on the board of the combined

firm when bidder and target are connected by means of shared directors. Moreover, director

retention is more likely when the M&A is not hostile, the bidder and target are in the same

industry and when the bidder is larger and acquires the target with an offer involving equity.

In addition, a larger bidding firm size, a higher cash ratio and a lower debt ratio are also

positively related to director retention. The above results support hypothesis 6 and are in line

with results in Ishii and Xuan (2010). However, one potential problem is that the number of

target director retention may be affected by the size of the bidder’s board. Larger boards may

be more likely to have extra positions for new directors than small and focused boards. In

order to remove the board size effect, we replace the dependent variable by the number of

target director retention scaled by the size of bidder board. The result in models (2) of Table

10 reveals that the connections-related variables are still positive and that the number of

connections is significant at the 5% level.7 As a robustness check, we use Heckman sample

selection models whereby the selection regression is the success versus failure of the M&A,

and we obtain similar results for the regression equation results. Lastly, Degree and Closeness

7 Since board size is missing for some company years, the sample size of model (2) is smaller.

20

on the company as well as the bidder’s CEO level are not significant when included in the

above models. In other words, contrary to the direct connections between bidder and target,

the general network position of the bidder or his CEO does not seem to affect target director

retention.


5. Conclusion.

In recent years, some scholars have applied graph theoretical methods in the research on the

impact of director networks on managerial decision making. They found relations between

networks and remuneration contracting, the managerial labour market (hiring and firing of top

management, attracting non-executive directors), corporate restructuring, and firm and fund

performance. In this paper, we examine the effect of the connections between the acquirer and

target firms on the takeover process, more specifically on M&A frequency, the M&A

negotiation success and duration, the means of payment in the offer, the M&A expected

performance (as reflected in the short term wealth effects of the bidder), the bidder’s CEO

compensation subsequent to the M&A, and target director retention rate in the merged

company. The idea is that direct connections enable both parties to gather information more

easily on the counter party which establishes trust, and that the overall network (which

includes the indirect connections) enable firms to scout for suitable takeover targets and

collect relevant information on the whole takeover market. We find that director networks

play an important role in UK takeovers in the following way: First, we exhibit strong evidence

on the fact that connections through directorships between bidder and acquirer lead to more

takeover activity. Not only direct connections between bidder and target are important, but so

are the indirect connections of the board and the CEO of the bidding firms. In a nutshell:

better connected companies are more active bidders. Second, the above conclusion raises the

question as to whether connected bidders just make more acquisition attempts or are more

successful in completing the M&A negotiations. We demonstrate that when a bidder and a

target have one or more directors in common, the probability that the takeover transaction will

be successfully completed significantly augments. Only direct connections have an impact on

21

the M&A process but not the proxies for indirect connections of information collection. Third,

connections also significantly reduce the time used in the negotiation process (both for

successful or failed negotiations). Fourth, we expect that connections yield an informational

advantage which could also build trust between the parties which would in turn be reflected in

the more frequent use of offers that involved equity. We confirm that equity is indeed used

more often when bidder and target are connected. Fifth, the market reaction to the M&A

announcement of the bidder is not related to connected takeovers. This suggests that the

market either does not pick up that the two parties involved are connected or that they do not

believe it to be important. Sixth, while earlier research found a positive relation between a

CEO’s level of connectedness and his remuneration, we do not find evidence that CEOs of

connected bidders are paid more subsequent to completing a connected M&A. Finally, the

target directors (without prior connections to the bidding firm) have a better chance to be

invited to the board of the combined firm when bidder and target were directly connected.

The paper has contributed to our understanding of M&As and director networks. At first sight,

interlocked directors and directors’ information collection ability (proxied by centrality

measures) makes the M&A process more efficient: the degree of connectedness increases the

number of M&A transactions, increases the successful completion rate, reduces the

negotiation time, and enables the bidder to offer equity. Still, it seems that the market does not

recognize the fact that the parties involved are connected or attaches little value to it as the

announcement share price reactions in connected M&As are small and not difference from

those of unconnected M&As.

22

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Faccio, M., and R. W. Masulis, 2005, The Choice of Payment Method in European Mergers and

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23

Table 1. (Connected) Acquisitions.

This table gives an overview of the number of acquisitions by year (Column (1)) over the period 1995 to

2012 and the percentage of acquisitions by year (based on all acquisitions over the whole period) (Column

(2)). The table also shows the number (and percentage) of connected acquisitions in which the bidder and

target firms share at least one director (Columns (3) and (4)). The last column shows the percentage of

connected acquisitions (considering all acquisitions) by year. Source: SDC.

(1)

Number of

acquisitions

(2)

Distribution of

all acquisitions

over time (%)

(3)

Number of connected

acquisitions

(4)

Distribution of

connected acquisitions

over time (%)

(5)

% of Connected

acquisitions by year

Year

1995 33 4.44 2 2.86 6.06

1996 49 6.59 7 10.00 14.29

1997 51 6.86 9 12.86 17.65

1998 65 8.75 7 10.00 10.77

1999 90 12.11 4 5.71 4.44

2000 66 8.88 1 1.43 1.52

2001 30 4.04 1 1.43 3.33

2002 21 2.83 1 1.43 4.76

2003 40 5.38 4 5.71 10.00

2004 27 3.63 3 4.29 11.11

2005 53 7.13 7 10.00 13.21

2006 42 5.65 5 7.14 11.90

2007 44 5.92 2 2.86 4.55

2008 41 5.52 3 4.29 7.32

2009 34 4.58 5 7.14 14.71

2010 32 4.31 4 5.71 12.50

2011 19 2.56 5 7.14 26.32

2012 6 0.81 0 0 0

Total 743 100 70 100

Average 9.42

24

Table 2. Acquisitions by Bidder and Target Industry.

This table shows the percentage of bidders and targets by industry. Source: SDC.

Bidder Industry Sector % Target Industry Sector %

Agriculture 0.54 Agriculture 0.40

Chemicals 4.17 Chemicals 4.17

Construction 3.23 Construction 4.17

Finance 28.94 Finance 23.01

Food 3.1 Food 2.15

Furniture 0.4 Furniture 0.54

Manufacturing 9.29 Manufacturing 11.57

Mining 4.71 Mining 5.38

Printing 4.31 Printing 3.63

Retailing 9.29 Retailing 10.63

Services 21.27 Services 23.55

Telecommunication 5.65 Telecommunication 5.38

Textile 1.35 Textile 1.75

Transportation 2.29 Transportation 1.88

Utilities 1.48 Utilities 1.75

25

Table 3. M&A Transaction Characteristics.

Panel A shows the statistics on the number of M&A announcements per bidder over the sample period.

Panel B reports the target’s attitude towards the deal. Friendly means that the target board recommends the

offer; Hostile reflects that the target board officially rejects the offer but that the bidder persists with the

takeover. Panel C reports the different types of means of payment in the acquisition: all cash, all equity, or

mixed offers. Panel D records the completion rate by subsample. Panel E reports the connections between

bidders and targets. The dummy variable Connected equals one if the bidder and target share at least one

director at the time of acquisition (according to the most recent information prior to the acquisition). The

number of connections at the board level gives the number of shared directors between the bidder and target.

St.dev. stands for standard deviation. Panel F presents the negotiation time of the acquisition which is

defined as the difference between the announcement and completion dates of the takeover. Panel G reports

the deal size (in million GBP), the relative deal size (deal size dividend by market value of the bidder), and

whether target and bidder belong to the same sector which we call a focused transaction (dummy= 1, and 0

otherwise). Panel H reports the bidder CARs for the event windows: [-1,+1], [-5,+5] and [-10,+10]. Panel I

reports the number of directors from the target joining the combined company after M&As. Note these

statistics have been adjusted for the number of common directors to avoid double counting. Source: SDC,

Datastream and BoardEX.

Panel A. Number of M&A Transactions by Bidder

Number of deals by

bidder

Number of

bidders Percentage

1 374 72.90 2 92 17.93 3 26 5.07 4 7 1.36 5 7 1.36 6 5 0.97 7 2 0.39

Total 513 100

Panel B. Attitude towards the Takeover

Attitude Frequency Percentage

Friendly 704 94.75

Hostile 39 5.25

Total 743 100

Panel C. Payment Method

Payment method Frequency Percentage

Cash only 294 46.30

Equity only 202 31.81

Mixed 139 21.89

Total 635 100

26

Panel D. Takeover Completion Rate

Group Completed Deals Total takeover

announcements Success rate

All 609 743 81.97%

Not-Connected 542 673 80.53%

Connected 67 70 95.71%

Sample/mean

difference test

Mean Standard Deviation T-statistic

Not-Connected 0.805 0.014 2.95

Connected 0.957 0.024

Panel E. Connectedness of Bidder and Target

N Mean St.dev. Min 25% Median 75% Max

Connected (dummy) 743 0.094 0.292 0 0 0 0 1

Number of connections 743 0.164 0.799 0 0 0 0 17

Number of connections

(for connected M&As) 70 1.743 2.019 1 1 1 2 17

Panel F. Negotiation Time


Successful deals 594 59.602 49.141 0 23 56 85 293

Withdraw offers 129 60.333 52.646 1 26 45 82 256

All 723 59.733 49.747 0 24 55 83 293

Panel G. Deal Size, Relative Deal Size, and Focused Transaction


Deal size (GBP m) 578 143.392 211.155 0.050 14.380 52.525 162.58 996.9

Relative deal size 544 1.733 20.770 0.0001 0.062 0.243 0.662 480.1368

Focused M&A 743 67.2% 47.0% 0 0 1 1 1

Panel H. Cumulative Abnormal Stock Returns for the Bidder

CAR (%) N Mean St.dev. Min 25% Median 75% Max

[-1,+1] 666 -0.47 6.35 -37.96 -2.36 0.00 1.34 63.75

[-5,+5] 666 -0.94 9.40 -64.85 -4.30 -0.13 1.89 94.32

[-10,+10] 666 -1.37 11.65 -72.28 -5.76 -0.21 3.22 52.75

Panel I. Target Director Retention


Retention (all) 743 1.292 3.287 0 0 0 1 36

Retention (connected) 70 2.729 4.370 0 0 1 3 18

Retention (unconnected) 673 1.143 3.119 0 0 0 1 36

8 The outlier is Lasmo plc which acquired Monument Oil and Gas plc with a market value of GBP 1.25b, which leads to the very high relative deal size. Lasmo was acquired by ENI two years later.

27

Table 4. Bidder Characteristics.

This table summarizes the corporate governance and financial information on the bidders. Degree and

Closeness are the centrality measures of the bidder in the director networks. They are calculated on the

company level (C) as well as director level (D) (see Section 3). This table also reports board size, return on

assets (ROA), cash-to-assets ratio, debt-to-assets ratio, and total assets (in millions GBP).


Degree (C) 341 6.21 5.17 0 2 5 9 29

Closeness (C) 311 0.41 0.14 0.07 0.25 0.43 0.55 0.59

Degree (D) 341 12.39 8.38 0 7 10 15 55

Closeness (D) 341 0.06 0.02 0.01 0.05 0.07 0.08 0.09

Board size 341 10.49 4.054 2 8 10 13 23

ROA (%) 615 4.44 6.53 -24.53 1.41 5.54 8.67 16.59

Cash-to-assets ratio (%) 511 31.36 26.13 0 10.71 23.47 47.91 99.46

Debt-to-assets ratio (%) 643 0.21 0.17 0 0.05 0.18 0.33 0.76

Total assets (in mil. GBP) 668 153.25 171.01 4.72 28.85 81.78 218.02 734.80

Table 5. Connected Bidders and Targets.

This table measures the number of directly connected firms through director interlocks for different

samples: a. our takeover sample and b. random samples of bidder and target matched-up groups of firms

(whereby the random samples are drawn from the universe of all listed UK firms). The first row (simulation

group (1)) reports the number of connections between the bidders in the sample and random targets. The

random targets belong to the same industry as the real target. For simulation group (2), we select a random

company as a pseudo bidder for each target in the sample. Then, we examine whether the two companies

are connected via directors. Simulation group (3) is based on a similar simulation exercise, but this time

both the bidder and the target are randomly selected. The final row is based on the bidders and targets in the

actual sample.

Bidder Target

Percentage of

connected deals

Simulation group (1) From M&A sample Random 4.38%

Simulation group (2) Random From M&A sample 0.00%

Simulation group (3) Random Random 2.63%

Sample group From M&A sample From M&A sample 9.42%

28

Table 6. The Number of M&As.

This table reports the OLS regression with the total number of M&As (panel A) and the cumulated number

of M&As (panel B) as the dependent variable. Degree and (normalized) Closeness are used to measure the

bidder’s centrality in the network. (C) and (D) stands for networks on the company level and the director

(here taken as the CEO) level, respectively. Board size measures the number of executive and

non-executive directors on the board. Relative boardsize is board size scaled by total assets. ROA is the

return to assets of the bidder. Cash to total assets is the total cash and cash equivalents divided by total

assets. Debt to total assets captures the bidder’s leverage. We measure the size of the bidder by its total

assets value (logarithm). Standard errors are between brackets; ***, **, * stand for statistical significance

at the 1%, 5% and 10% level, respectively.

Panel A: Total Number of M&As

Average Degree (C) of bidder 0.044*

(0.023)

Average Closeness (C) of bidder 0.996

(0.673)

Average Degree (D) of bidder 0.017

(0.015)

Average Closeness (D) of bidder 4.742

(3.610)

Average Board Size -0.004 0.013 0.006 0.013

(0.034) (0.035) (0.034) (0.032)

Average ROA 0.001 -0.001 0.001 0.002

(0.007) (0.008) (0.007) (0.007)

Average Cash-to-total assets 0.003 0.002 0.003 0.004

(0.003) (0.004) (0.003) (0.003)

Average Debt-to-total assets 1.307** 1.396** 1.349** 1.291**

(0.564) (0.622) (0.568) (0.567)

Average Total assets (Logarithm) 0.011 0.034 0.032 0.037

(0.060) (0.064) (0.059) (0.058)

Number of Observations 191 169 191 191

R-squared 0.0773 0.0704 0.0659 0.0683

29

Panel B: Cumulative Number of M&As

Degree (C) of bidder 0.079***

(0.018)

Closeness (C) of bidder 0.954*

(0.568)

Degree (D) of bidder 0.023**

(0.011)

Closeness (D) of bidder 6.952**

(3.111)

Relative board size 0.403 0.914** 0.653* 0.840**

(0.357) (0.386) (0.376) (0.349)

ROA 0.002 0.001 0.002 0.002

(0.005) (0.006) (0.006) (0.006)

Cash-to-total assets 0.002 0.002 0.002 0.003

(0.003) (0.003) (0.003) (0.003)

Debt-to-total assets 1.382*** 1.283*** 1.403*** 1.188***

(0.429) (0.484) (0.445) (0.444)

Total assets (Logarithm) -0.052 0.023 0.002 0.027

(0.045) (0.045) (0.045) (0.041)


R-squared 0.1645 0.1078 0.1125 0.1155

30

Table 7. The Takeover Success Rate.

This table reports the logit regression results of the success rate of M&A transactions: the dependent

variable equals 1 if the M&A was successful, and 0 if withdrawn. The network variables are: Connected

(dummy) is a dummy variable which equals one if the bidder and target is connected via common

director(s); Number of connections is the number of common directors; Degree and (normalized)

Closeness measure the bidder’s centrality in the network with (C) and (D) representing networks at the

company level and CEO level, respectively. We control for: Same sector (dummy) equals one if the bidder

and target belong to the same industry; Hostile (dummy) is one if the target’s board rejects the bid; All cash

payment (dummy) equals one if the transaction is performed by means of an all-cash payment, and zero in

case of all-equity or mixed payment; Relative deal size is the transaction value in GBP scaled by the market

capitalization of bidding company. Standard errors are between brackets; ***, **, * stand for statistical

significance at the 1%, 5% and 10% level, respectively.

(1) (2) (3) (4) (5) (6)

Bidder and target are 0.834**

connected (dummy) (0.364)

Number of connections 0.317*

between bidder and target (0.188)

Degree (C) of bidder 0.091***

(0.033)

Closeness (C) of bidder 0.716

(0.802)

Degree (D) bidder 0.046**

(0.021)

Closeness (D) of bidder 3.985

(4.573)

Same sector (dummy) -0.046 -0.047 -0.220 -0.149 -0.222 -0.228

(0.157) (0.156) (0.264) (0.269) (0.263) (0.260)

Hostile (dummy) -1.527*** -1.524*** -1.784*** -1.704*** -1.746*** -1.673***

(0.253) (0.253) (0.443) (0.423) (0.443) (0.419)

All cash paym. (dummy) 0.295** 0.285** 0.072 0.299 0.198 0.246

(0.143) (0.142) (0.236) (0.231) (0.223) (0.220)

Relative deal size -0.0004 -0.0005 0.0001 0.001 0.0002 0.001

(0.003) (0.003) (0.005) (0.004) (0.005) (0.004)

Number of Obs. 542 542 256 231 256 256

R-squared 0.1186 0.1115 0.161 0.1263 0.1407 0.1139

31

Table 8. Negotiation Time.

Panel A reports the (left censored) Tobit regression results of the negotiation time in M&A transactions.

Negotiation time is the difference between announcement and accomplishment dates. Two network

variables are used to measure the connectedness between the bidder and target: Connected (dummy) equals

1 if the bidder and target is connected via common director(s) and 0 otherwise. Number of connections is

the number of common directors between bidder and target. Degree and (normalized) closeness are used to

measure the bidder’s centrality in the network. (C) stands for networks on the company level. We use four

variables to control for deal nature and for the bidder’s characteristics: Same sector (dummy) equals 1 if the

bidder and target belong to the same industry; Hostile (dummy) is 1 if the target’s board rejects the bid (for

whatever reason); All cash payment (dummy) equals 1 if an all-cash offer is made, and 0 in case of

all-equity or mixed offers; Relative deal size is the transaction value in GBP scaled by the market

capitalization of bidding company; Cash-to-total assets is calculated as the total cash and cash equivalents

divided by total assets; Debt-to-total assets is the capital structure of bidder. The bidder’s size is the

logarithm of total assets value (book value). Panel B expands the previous models by including the

centrality measures at the company level. Standard errors are between brackets; ***, **, * stand for

statistical significance at the 1%, 5% and 10% level, respectively.

Panel A: Negotiation time

Success All

Bidder and Target are -21.141** -21.501**

connected (dummy) (8.787) (8.902)

Number of connections -9.955** -10.119**

between bidder and target (4.899) (4.965)

Same sector (dummy) -8.973 -9.401 -7.690 -8.041

(5.920) (5.945) (5.587) (5.606)

Hostile (dummy) 36.554** 37.297** 9.714 10.254

(17.564) (17.597) (10.782) (10.795)

All cash payment (dummy) -42.360*** -42.040*** -37.011*** -36.746***

(5.821) (5.829) (5.381) (5.387)

Relative deal size 1.960 1.987 -0.043 -0.034

(1.703) (1.707) (0.788) (0.790)

ROA 0.098 0.099 0.100 0.101

(0.142) (0.142) (0.137) (0.137)

Cash-to-total assets 0.056 0.074 0.042 0.057

(0.116) (0.117) (0.109) (0.109)

Debt-to-total assets 0.364 3.234 -0.749 1.656

(19.119) (19.085) (17.852) (17.829)

Total assets (logarithm) 8.815*** 8.705*** 6.787*** 6.684***

(1.676) (1.678) (1.574) (1.575)


R-squared 0.0244 0.0238 0.016 0.0155

32

Panel B: Negotiation time

Success All

Connected (dummy) -26.635** -29.992**

(13.256) (13.324)

Number of connections -18.860** -21.006**

(8.678) (8.735)

Degree (C) 2.759** 2.811** 2.076** 2.126**

(1.099) (1.097) (1.031) (1.030)

Closeness (C) -75.141** -77.875** -57.779** -60.904**

(30.220) (30.101) (28.252) (28.160)

Same sector (dummy) -5.455 -6.317 -5.110 -5.939

(8.910) (8.949) (8.585) (8.620)

Hostile (dummy) 82.080** 83.151** 33.939* 34.520*

(37.051) (36.981) (18.114) (18.072)

All cash payment (dummy) -40.198*** -40.173*** -38.729*** -38.671***

(8.568) (8.560) (7.990) (7.984)

Relative deal size 0.114 0.078 -0.329 -0.350

(2.170) (2.167) (1.244) (1.243)

ROA -0.279 -0.249 -0.115 -0.086

(0.325) (0.326) (0.316) (0.316)

Cash-to-total assets -0.243 -0.228 -0.125 -0.109

(0.193) (0.193) (0.172) (0.172)

Debt-to-total assets 3.998 8.469 3.523 7.926

(25.918) (25.810) (24.697) (24.600)

Total assets (logarithm) 2.657 2.348 2.422 2.111

(3.004) (3.003) (2.795) (2.795)


R-squared 0.0255 0.026 0.0193 0.0198

33

Table 9. The Means of Payment.

Panel A presents the results of logit regressions on the type of means of payment in the offer. The dependent

variable in Models (1) equals 1 if the M&A is concluded with an offer that includes equity (an all-equity

transaction or a mixed payment), and equals zero in case of an all-cash payment. In Models (2), we test the

use of offers involving cash (in case of cash or mixed payment the dummy equals one, and equals zero in

case of an all equity offer). Connected (dummy) equals one if bidder and target is connected via common

director(s). Number of connections is the number of directors that a bidder and target share. Same sector

(dummy) equals one if the bidder and target are from the same industry. Hostile (dummy) is one if the offer

is (initially) rejected by the target’s board. Relative deal size is the transaction value scaled by the market

capitalization of bidding company. ROA is the bidder’s return to assets. Cash-to-total assets is the total cash

and cash equivalents divided by total assets. Debt-to-total assets is of the bidder’s leverage. Size is total

assets’ book value (logarithm). Panel B gives the results of Tobit (left and right censored) regressions on

the means of payment. The dependent variable is the percentage of cash in the offer. Standard errors are

between brackets; ***, **, * stand for statistical significance at the 1%, 5% and 10% level, respectively.

Panel A: Type of Offer

(1)

All equity or mixed payment

(2)

All cash or mixed payment

Connected (dummy) 0.646* -0.936**

(0.357) (0.366)

Number of connections 0.3011 -0.343*

(0.204) (0.203)

Same sector (dummy) 0.168 0.177 -0.147 -0.151

(0.228) (0.228) (0.259) (0.258)

Hostile (dummy) 0.332 0.315 -0.723 -0.656

(0.463) (0.462) (0.463) (0.461)

Relative deal size 0.254* 0.251* -0.003 -0.002

(0.137) (0.137) (0.033) (0.032)

ROA -0.007 -0.007 0.015** 0.015**

(0.006) (0.006) (0.006) (0.006)

Cash to total assets 0.003 0.002 -0.002 -0.002

(0.004) (0.004) (0.005) (0.005)

Debt to total assets 0.227 0.149 1.277 1.411

(0.734) (0.731) (0.878) (0.872)

Total assets (logarithm) -0.217*** -0.212*** 0.300*** 0.289***

(0.065) (0.065) (0.077) (0.075)


R-squared 0.0670 0.0651 0.1077 0.1004

34

Panel B: Percentage of Cash in Offer

Connected (dummy) -87.504**

(35.802)

Number of connections -36.905*

(19.948)

Same sector (dummy) -16.747 -17.626

(22.485) (22.630)

Hostile (dummy) -55.774 -53.178

(43.862) (44.009)

Relative deal size -3.310 -3.280

(3.084) (3.100)

ROA 1.148* 1.160*

(0.601) (0.605)

Cash to total assets -0.218 -0.166

(0.434) (0.437)

Debt to total assets 31.656 41.564

(71.288) (71.364)

Total assets (logarithm) 29.581*** 29.078***

(6.920) (6.913)

Number of Observations 395 395

R-squared 0.033 0.0312

35

Table 10. Target Director Retention in the Combined Firm.

The table reports the results of regressions for the retention of the targets’ directors in the combined

company subsequent to the M&A. The dependent variable in Models (1) (Poisson model) is the number of

unconnected targets’ directors joining the combined company’s board while this variable in Models (2)

(OLS) is the number of retained directors as a percentage of the bidder’s board. Two variables are used to

measure the connections between bidder and target: Connected (dummy), a dummy variable which equals 1

if bidder and target are connected via (a) director(s); Number of connections, the number of directors which

bidder and target have in common. Same sector (dummy) equals one if bidder and target are from the same

industry. Hostile (dummy) is 1 if the M&A is considered as hostile by the target. All cash payment (dummy)

equals one if the transaction is completed with an all-cash payment and zero in case of an all-equity or

mixed offer. Relative deal size is the transaction value scaled by the market capitalization of bidding

company. ROA is the bidder’s return on assets. Cash-to-total assets is calculated as the total cash and cash

equivalents divided by total assets. Debt-to-total assets is used to measure the capital structure of bidder.

The size of the bidder is the total assets value (logarithm). Standard errors are between brackets; ***, **, *

stand for statistical significance at the 1%, 5% and 10% level, respectively.

(1)

Total Retention

(2)

Total Retention/Board Size

Connected (dummy) 0.653*** 0.127

(0.103) (0.080)

Number of connections 0.381*** 0.094**

(0.046) (0.044)

Same sector (dummy) 0.400*** 0.446*** 0.081 0.088*

(0.094) (0.095) (0.054) (0.054)

Hostile (dummy) -0.484** -0.486** -0.111 -0.110

(0.218) (0.218) (0.120) (0.119)

All Cash -0.505*** -0.490*** -0.056 -0.056

(0.086) (0.086) (0.050) (0.049)

Relative deal size -0.023 -0.023 -0.001 0.0003

(0.018) (0.018) (0.008) (0.008)

ROA -0.006*** -0.006*** 0.003 0.003

(0.002) (0.002) (0.002) (0.002)

Cash-to-total assets 0.004** 0.003* 0.002* 0.002*

(0.002) (0.002) (0.001) (0.001)

Debt-to-total assets -1.051*** -1.089*** -0.067 -0.088

(0.302) (0.299) (0.155) (0.154)

Total assets (logarithm) 0.108*** 0.104*** -0.007 -0.006

(0.024) (0.024) (0.016) (0.015)


R-squared 0.0744 0.0806 0.0566 0.0662

36

Appendix A: Variable Definitions. Variable Name Description Source

Dependent variables

Takeover success rate Equals 1 if the M&A was successful, and 0 if withdrawn Thomson Reuters SDC

Negotiation time

The gap between the announcement of the deal

completion or failure of the negotiations and the first

public announcement of that takeover negotiations are

taking place

Thomson Reuters SDC

All equity or mixed payment

Equals 1 if the M&A is completed with an all-equity or

mixed payment (equity and cash, and potentially with

loan notes)

Thomson Reuters SDC

Cumulative abnormal stock returns (CARs) Cumulative abnormal stock return over an event window

around the first public announcement Datastream, calculation

Total director retention The number of target directors joining the board of the

bidding company subsequent to the takeover transaction

BoardEx, Manifest, Thomson Reuters

One Banker

Director retention as a percentage of bidder board Total director retention divided by the bidder’s board size

priori to M&A

Boardex, Manifest, Thomson Reuters One

Banker

Centrality and connections

Degree (C) Number of companies connected by common directors Boardex,Manifest, calculation

Degree (D) Number of directors connected to the bidder CEO Boardex,Manifest, calculation

(normalized) Closeness (C)

The inverse of the sum of geodesic distances from bidder

to all other companies, scaled by total number of

reachable companies in the network

Boardex,Manifest, calculation

(normalized) Closeness (D)

The inverse of the sum of geodesic distances from bidder

CEO to all other directors, scaled by total number of

reachable directors in the network

Boardex,Manifest, calculation

Connected (dummy) Equals 1 if the bidder and target have directors in

common Boardex,Manifest, Thomson Reuters SDC

Number of connections The number of shared directors between bidder and target Boardex,Manifest, Thomson Reuters SDC

Number of connections/directors Number of connections divided by the bidder’s board size Boardex,Manifest, Thomson Reuters SDC

M&A characteristics

Same sector (dummy) Equals 1 if the bidder and target belong to same sector Thomson Reuters SDC

Hostile (dummy) Equals 1 if the M&A is hostile Thomson Reuters SDC

All cash payment (dummy) Equals 1 if the M&A is paid in cash only Thomson Reuters SDC

Relative deal size Transaction value (in GBP) scaled by the market

capitalization of bidding company Thomson Reuters SDC, calculation

Total relative deal size

The sum of the transaction values of all M&As in a

financial year scaled by the market capitalization of

bidding company

Thomson Reuters SDC, calculation

Multiple deals The number of M&As announced on the same day by the

bidder Thomson Reuters SDC

(Cumulative) number of M&As The number of M&As by a bidder over the sample period Thomson Reuters SDC

Bidder characteristics

ROA (%) Net income prior to tax and interest divided by total Datastream

37

assets, and then multiplied by 100

Relative board size Board size divided by total number of directors on the

board

Boardex,Manifest,Datastream,

calculation

Cash to total assets (%) Total cash and cash equivalents divided by total assets,

and then multiplied by 100 Datastream

Debt to total assets (%) Sum of short-term and long-term debt divided by total

assets, and then multiplied by 100 Datastream

Total assets

Sum of total current assets, long-term receivables,

investment in unconsolidated subsidiaries, other

investments, net property plant and equipment and other

assets. We take the logarithm of total assets.

Datastream

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